code: def get_permutation(
      decomposition: Dict[str, List[int]]
  ) -> Optional[List[int]]:
      """Construct permutation to reorder the parameters such that:
  
      1) the parameters for each context are contiguous.
      2) The parameters for each context are in the same order
  
      Args:
          decomposition: A dictionary mapping context names to a list of
              parameters.
      Returns:
          A permutation to reorder the parameters for (1) and (2).
          Returning `None` means that ordering specified in `decomposition`
          satisfies (1) and (2).
      """
      permutation = None
      if not all(
          is_contiguous(indices=active_parameters)
          for active_parameters in decomposition.values()
      ):
          permutation = _create_new_permutation(
              decomposition=decomposition
          )
      else:
          same_order = True
          expected_order = get_order(
              indices=next(iter(decomposition.values()))
          )
          for active_parameters in decomposition.values():
              order = get_order(indices=active_parameters)
              if order != expected_order:
                  same_order = False
                  break
          if not same_order:
              permutation = _create_new_permutation(
                  decomposition=decomposition
              )
      return permutation
  
imports: from typing import Any, Dict, List, Optional
  import torch
  from gpytorch.constraints import Positive
  from gpytorch.kernels.kernel import Kernel
  from gpytorch.kernels.matern_kernel import MaternKernel
  from gpytorch.priors.torch_priors import GammaPrior
  from linear_operator.operators import DiagLinearOperator
  from linear_operator.operators.dense_linear_operator import DenseLinearOperator
  from torch import Tensor
  from torch.nn import ModuleList
label_desc: Use image node_id 3 for calling a global function with example usage: get_permutation(decomposition) and returns: permutation